Biomarker Of Posttraumatic Stress Disorder Research Articles

Volumetric Integrated Classification Index: An Integrated Voxel-Based Morphometry and Machine Learning Interpretable Biomarker for Post-Traumatic Stress Disorder.

PTSD is a complex mental health condition triggered by individuals' traumatic experiences, with long-term and broad impacts on sufferers' psychological health and quality of life. Despite decades of research providing partial understanding of the pathobiological aspects of PTSD, precise neurobiological markers and imaging indicators remain challenging to pinpoint. This study employed VBM analysis and machine learning algorithms to investigate structural brain changes in PTSD patients. Data were sourced ADNI-DoD database for PTSD cases and from the ADNI database for healthy controls. Various machine learning models, including SVM, RF, and LR, were utilized for classification. Additionally, the VICI was proposed to enhance model interpretability, incorporating SHAP analysis. The association between PTSD risk genes and VICI values was also explored through gene expression data analysis. Among the tested machine learning algorithms, RF emerged as the top performer, achieving high accuracy in classifying PTSD patients. Structural brain abnormalities in PTSD patients were predominantly observed in prefrontal areas compared to healthy controls. The proposed VICI demonstrated classification efficacy comparable to the optimized RF model, indicating its potential as a simplified diagnostic tool. Analysis of gene expression data revealed significant associations between PTSD risk genes and VICI values, implicating synaptic integrity and neural development regulation. This study reveals neuroimaging and genetic characteristics of PTSD, highlighting the potential of VBM analysis and machine learning models in diagnosis and prognosis. The VICI offers a promising approach to enhance model interpretability and guide clinical decision-making. These findings contribute to a better understanding of the pathophysiological mechanisms of PTSD and provide new avenues for future diagnosis and treatment.

Longitudinal associations between early post-injury serum BDNF levels and the development of post-traumatic stress disorder over two years

BackgroundThis study investigated the longitudinal associations between serum BDNF (sBDNF) levels measured early after injury and the onset of post-traumatic stress disorder (PTSD) across two years. MethodsPatients with moderate to severe physical injuries were enrolled from a trauma center. At baseline, sBDNF levels were measured and a comprehensive socio-demographic and clinical data were collected. The range of time from physical injuries to blood collection was 1–28 days, with a median (IQR) of 8.0 (6.0) days. PTSD diagnoses were determined at 3, 6, 12, and 24 months post-injury using the CAPS-5. Linear regression analyses assessed the relationship between sBDNF levels and PTSD diagnoses. ResultsOut of 923 patients, 112 (12.1 %) developed PTSD during the study. Prevalence rates were 8.8 % at 3 months, 7.6 % at 6 months, 4.8 % at 12 months, and 3.7 % at 24 months. Significantly, lower sBDNF levels were associated with PTSD at 12 and 24 months, after adjusting for covariates and applying Bonferroni corrections, but not at earlier assessments. LimitationsFocusing on patients with moderate to severe injuries from a single center may limit the findings' generalizability. ConclusionEarly post-injury sBDNF levels are predictive biomarkers for PTSD, especially significant at 12 and 24 months post-injury.

MicroRNAs as potential biomarkers for diagnosis of post-traumatic stress disorder.

Post-traumatic stress disorder is a mental disorder caused by exposure to severe traumatic life events. Currently, there are no validated biomarkers or laboratory tests that can distinguish between trauma survivors with and without post-traumatic stress disorder. In addition, the heterogeneity of clinical presentations of post-traumatic stress disorder and the overlap of symptoms with other conditions can lead to misdiagnosis and inappropriate treatment. Evidence suggests that this condition is a multisystem disorder that affects many biological systems, raising the possibility that peripheral markers of disease may be used to diagnose post-traumatic stress disorder. We performed a PubMed search for microRNAs (miRNAs) in post-traumatic stress disorder (PTSD) that could serve as diagnostic biomarkers and found 18 original research articles on studies performed with human patients and published January 2012 to December 2023. These included four studies with whole blood, seven with peripheral blood mononuclear cells, four with plasma extracellular vesicles/exosomes, and one with serum exosomes. One of these studies had also used whole plasma. Two studies were excluded as they did not involve microRNA biomarkers. Most of the studies had collected samples from adult male Veterans who had returned from deployment and been exposed to combat, and only two were from recently traumatized adult subjects. In measuring miRNA expression levels, many of the studies had used microarray miRNA analysis, miRNA Seq analysis, or NanoString panels. Only six studies had used real time polymerase chain reaction assay to determine/validate miRNA expression in PTSD subjects compared to controls. The miRNAs that were found/validated in these studies may be considered as potential candidate biomarkers for PTSD and include miR-3130-5p in whole blood; miR-193a-5p, -7113-5p, -125a, -181c, and -671-5p in peripheral blood mononuclear cells; miR-10b-5p, -203a-3p, -4488, -502-3p, -874-3p, -5100, and -7641 in plasma extracellular vesicles/exosomes; and miR-18a-3p and -7-1-5p in blood plasma. Several important limitations identified in the studies need to be taken into account in future studies. Further studies are warranted with war veterans and recently traumatized children, adolescents, and adults having PTSD and use of animal models subjected to various stressors and the effects of suppressing or overexpressing specific microRNAs.

Towards predicting PTSD symptom severity using portable EEG-derived biomarkers.

Posttraumatic Stress Disorder (PTSD) is a heterogeneous mental health disorder that occurs following traumatic experience. Understanding its neurobiological basis is crucial to advance early diagnosis and treatment. Electroencephalography (EEG) can be used to explore the neurobiological basis of PTSD. However, only limited research has explored mobile EEG, which is important for scalability. This proof-of-concept study delves into mobile EEG-derived biomarkers for PTSD and their potential implications. Over four weeks, we measured PTSD symptoms using the PTSD checklist for DSM-5 (PCL-5) at multiple timepoints, and we recorded multiple EEG sessions from 21 individuals using a mobile EEG device. In total, we captured 38 EEG sessions, each comprising two recordings that lasted approximately 180 seconds, to evaluate reproducibility. Next, we extracted Shannon entropy, as a measure of the randomness or unpredictability of the signal and spectral power for the fronto-temporal regions of interest, including electrodes at AF3, AF4, T7, and T8 for each EEG recording session. We calculated the partial correlation between the EEG variables and PCL-5 measured closest to the EEG session, using age, sex, and the grouping variable 'batch' as covariates. We observed a significant negative correlation between Shannon entropy in fronto-temporal regions and PCL-5 scores. Specifically, this association was evident in the AF3 (r = -0.456, FDR-corrected p = 0.01), AF4 (r = -0.362, FDR-corrected p = 0.04), and T7 (r = -0.472, FDR-corrected p = 0.01) regions. Additionally, we found a significant negative association between the alpha power estimated from AF4 and PCL-5 (r=-0.429, FDR-corrected p=0.04). Our findings suggest that EEG data acquired using a mobile EEG device is associated with PTSD symptom severity, offering valuable insights into the neurobiological mechanisms underlying PTSD.

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning

Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015, terrorist attacks in Paris. The interviews, conducted 5–11 months after the event, include individuals from similar socioeconomic backgrounds exposed to the same incident, responding to identical questions and using uniform PTSD measures. Using this dataset to collect nuanced insights that might be clinically relevant, we propose a three-step interdisciplinary methodology that integrates expertise from psychiatry, linguistics, and the Natural Language Processing (NLP) community to examine the relationship between language and PTSD. The first step assesses a clinical psychiatrist's ability to diagnose PTSD using interview transcription alone. The second step uses statistical analysis and machine learning models to create language features based on psycholinguistic hypotheses and evaluate their predictive strength. The third step is the application of a hypothesis-free deep learning approach to the classification of PTSD in our cohort. Results show that the clinical psychiatrist achieved a diagnosis of PTSD with an AUC of 0.72. This is comparable to a gold standard questionnaire (Area Under Curve (AUC) ≈ 0.80). The machine learning model achieved a diagnostic AUC of 0.69. The deep learning approach achieved an AUC of 0.64. An examination of model error informs our discussion. Importantly, the study controls for confounding factors, establishes associations between language and DSM-5 subsymptoms, and integrates automated methods with qualitative analysis. This study provides a direct and methodologically robust description of the relationship between PTSD and language. Our work lays the groundwork for advancing early and accurate diagnosis and using linguistic markers to assess the effectiveness of pharmacological treatments and psychotherapies.

Resting heart rate associations with violence exposure and posttraumatic stress symptoms: sex differences in children

BackgroundTraumatic events experienced in childhood can lead to increased risk of cardiovascular disorders in adulthood. Black Americans are disproportionately affected, as they are at increased risk for experiencing childhood trauma and cardiovascular diseases in adulthood. One of the hypothesized mechanisms of this association is through long-lasting dysregulation of the autonomic nervous system, a hallmark physiological biomarker of posttraumatic stress disorder (PTSD), which is twice as prevalent in women compared to men.MethodsNinety-one, majority Black American children, aged 9 were recruited to be a part of our longitudinal study of child development at research centers in Atlanta, GA and Detroit, MI. Resting HR was measured through a electrocardiogram (ECG) recording using the Biopac MP150. Self-report measures of violence exposure and PTSD symptoms were administered by research staff.ResultsChildren with more violence exposure reported increased PTSS as well as lower resting HR. Regression analysis showed evidence of sex modifying this relationship, (B = -0.64, p < 0.05), such that the association between resting HR and PTSS was stronger in girls than in boys. In our exploratory analysis with standard clinical cutoffs of resting HR, the normative HR group was found to significantly moderate the relationship between violence exposure and PTSS in boys, (B = -2.14, p < 0.01), but not girls (B = -0.94, p = 0.27).ConclusionIn our sample of primarily Black urban children, we found that violence exposure was associated with slower, more adult-like HR, that girls showed greater PTSS associated with slower HR while boys did not, and that girls with lower than normative HR showed significantly higher PTSS compared to girls with normative HR. Our sample’s demonstration of psychological consequences in addition to the physiological implications could provide new information about a psychobiological sequelae of violence exposure.

Application of machine learning techniques in the diagnostic approach of PTSD using MRI neuroimaging data: A systematic review

BackgroundAt present, the diagnosis of post-traumatic stress disorder（PTSD） mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging（DTI）are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people's interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis. ObjectivesAccording to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls). MethodsWe searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors. ResultsAll ML techniques have an diagnostic accuracy rate above 70%，and support vector machine（SVM） are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis. ConclusionIn contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.

Plasma and Platelet Brain-Derived Neurotrophic Factor (BDNF) Levels in Bipolar Disorder Patients with Post-Traumatic Stress Disorder (PTSD) or in a Major Depressive Episode Compared to Healthy Controls.

Post-traumatic stress disorder (PTSD) is a highly disabling mental disorder arising after traumatism exposure, often revealing critical and complex courses when comorbidity with bipolar disorder (BD) occurs. To search for PTSD or depression biomarkers that would help clinicians define BD presentations, this study aimed at preliminarily evaluating circulating brain-derived-neurotrophic factor (BDNF) levels in BD subjects with PTSD or experiencing a major depressive episode versus controls. Two bloodstream BDNF components were specifically investigated, the storage (intraplatelet) and the released (plasma) ones, both as adaptogenic/repair signals during neuroendocrine stress response dynamics. Bipolar patients with PTSD (n = 20) or in a major depressive episode (n = 20) were rigorously recruited together with unrelated healthy controls (n = 24) and subsequently examined by psychiatric questionnaires and blood samplings. Platelet-poor plasma (PPP) and intraplatelet (PLT) BDNF were measured by ELISA assays. The results showed markedly higher intraplatelet vs. plasma BDNF, confirming platelets' role in neurotrophin transport/storage. No between-group PPP-BDNF difference was reported, whereas PLT-BDNF was significantly reduced in depressed BD patients. PLT-BDNF negatively correlated with mood scores but not with PTSD items like PPP-BDNF, which instead displayed opposite correlation trends with depression and manic severity. Present findings highlight PLT-BDNF as more reliable at detecting depression than PTSD in BD, encouraging further study into BDNF variability contextually with immune-inflammatory parameters in wider cohorts of differentially symptomatic bipolar patients.

Arriving at One Goal Is the Starting Point to Another: Identification of Prognostic Biomarkers for Posttraumatic Stress Disorder Starts a Long Journey Toward Translation

Arriving at One Goal Is the Starting Point to Another: Identification of Prognostic Biomarkers for Posttraumatic Stress Disorder Starts a Long Journey Toward Translation

PTSD biomarkers: Neuroendocrine signaling to epigenetic variants.

Posttraumatic stress disorder (PTSD) is characterized by exposure to traumatic events and involves symptom domains such as intrusive thoughts, avoidant behaviors, negative mood, and cognitive dysfunction. The disorder can be chronic and debilitating, and the heterogenous nature and varied presentation of PTSD has afforded difficulty in determining efficacious treatment. The ability to identify biomarkers for PTSD risk, prognosis, or for the purposes of treatment, would be highly valuable. There is evidence for peripheral biomarkers related to the hypothalamic-pituitary-adrenal axis, the immune system, neurotransmitters and neurohormones, while genome and epigenome wide association studies have identified genes of interest relating to neurocircuitry, monoaminergic function, and the immune system. Importantly, however, reproducibility is a persistent issue. Considerations for future research include the need for well-powered and well-designed studies to determine directionality, in addition to considering biomarkers as they relate to symptom domains and the spectrum of symptom severity rather than dichotomous diagnostic outcomes. We conclude by recommending the staging of biological processes and PTSD symptoms, from subsyndromal to chronic, which could eventually facilitate selection of personalized treatment interventions for individuals with PTSD, in addition to serving as a future framework for biomarker data.

Levels of Arachidonic Acid-Derived Oxylipins and Anandamide Are Elevated Among Military APOE ɛ4 Carriers With a History of Mild Traumatic Brain Injury and Post-Traumatic Stress Disorder Symptoms.

Currently approved blood biomarkers detect intracranial lesions in adult patients with mild to moderate traumatic brain injury (TBI) acutely post-injury. However, blood biomarkers are still needed to help with a differential diagnosis of mild TBI (mTBI) and post-traumatic stress disorder (PTSD) at chronic post-injury time points. Owing to the association between phospholipid (PL) dysfunction and chronic consequences of TBI, we hypothesized that examining bioactive PL metabolites (oxylipins and ethanolamides) would help identify long-term lipid changes associated with mTBI and PTSD. Lipid extracts of plasma from active-duty soldiers deployed to the Iraq/Afghanistan wars (control = 52, mTBI = 21, PTSD = 34, and TBI + PTSD = 13) were subjected to liquid chromatography/mass spectrometry analysis to examine oxylipins and ethanolamides. Linear regression analyses followed by post hoc comparisons were performed to assess the association of these lipids with diagnostic classifications. Significant differences were found in oxylipins derived from arachidonic acid (AA) between controls and mTBI, PTSD, and mTBI + PTSD groups. Levels of AA-derived oxylipins through the cytochrome P450 pathways and anandamide were significantly elevated among mTBI + PTSD patients who were carriers of the apolipoprotein E E4 allele. These studies demonstrate that AA-derived oxylipins and anandamide may be unique blood biomarkers of PTSD and mTBI + PTSD. Further, these AA metabolites may be indicative of an underlying inflammatory process that warrants further investigation. Future validation studies in larger cohorts are required to determine a potential application of this approach in providing a differential diagnosis of mTBI and PTSD in a clinical setting.

Discovery and replication of blood-based proteomic signature of PTSD in 9/11 responders

Proteomics provides an opportunity to develop biomarkers for the early detection and monitoring of post-traumatic stress disorder (PTSD). However, research to date has been limited by small sample sizes and a lack of replication. This study performed Olink Proseek Multiplex Platform profiling of 81 proteins involved in neurological processes in 936 responders to the 9/11 disaster (mean age at blood draw = 55.41 years (SD = 7.93), 94.1% white, all men). Bivariate correlations and elastic net regressions were used in a discovery subsample to identify concurrent associations between PTSD symptom severity and the profiled proteins, and to create a multiprotein composite score. In hold-out subsamples, nine bivariate associations between PTSD symptoms and differentially expressed proteins were replicated: SKR3, NCAN, BCAN, MSR1, PVR, TNFRSF21, DRAXIN, CLM6, and SCARB2 (|r| = 0.08–0.17, p < 0.05). There were three replicated bivariate associations between lifetime PTSD diagnosis and differentially expressed proteins: SKR3, SIGLEC, and CPM (OR = 1.38–1.50, p < 0.05). The multiprotein composite score retained 38 proteins, including 10/11 proteins that replicated in bivariate tests. The composite score was significantly associated with PTSD symptom severity (β = 0.27, p < 0.001) and PTSD diagnosis (OR = 1.60, 95% CI: 1.17–2.19, p = 0.003) in the hold-out subsample. Overall, these findings suggest that PTSD is characterized by altered expression of several proteins implicated in neurological processes. Replicated associations with TNFRSF21, CLM6, and PVR support the neuroinflammatory signature of PTSD. The multiprotein composite score substantially increased associations with PTSD symptom severity over individual proteins. If generalizable to other populations, the current findings may inform the development of PTSD biomarkers.

Sex-related difference of association of mitochondrial DNA copy number with PTSD in U.S. service members

Gender differences in the lifetime prevalence of post-traumatic stress disorder (PTSD) have been well described with rates reported as approximately 10%–12% in females and 5%–6% in males (Olff, 2017). This study examined whether the sex-related difference of mitochondrial DNA copy number (mtDNAcn), an emerging systemic index of mitochondrial biogenesis and function can serve as a potential biomarker for PTSD. Leukocyte mtDNAcn of service members with PTSD (male = 127, female = 24) or without PTSD (male = 621, female = 78) was assessed using a TaqMan assay. The results were validated by the absolute quantification of QX-200 droplet digital PCR (ddPCR). PTSD symptoms and symptom severity were assessed using the PTSD Checklist (PCL), a 17-item, DSM-based, self-report questionnaire with well-established validity and reliability. DSM-IV criteria and PTSD were determined by PCL total score. We found that mtDNAcn of female subjects with PTSD was significantly higher compared to either male or female non-PTSD controls or male subjects with PTSD (p < 0.05). There was no significant difference in mtDNAcn between males with PTSD and male/female controls without PTSD. Using in vitro cultured SH-SY5Y cells (human neuroblastoma), we demonstrated that estrogen (Estro) treatment significantly decreased mtDNAcn (P < 0.001) compared to the vehicle control. We also found that pre-treatment with either synthetic glucocorticoid dexamethasone (Dex) or Estro blocker tamoxifen (Tamox) attenuated the estrogen-induced decreases of mtDNAcn. Our data suggest that mtDNAcn may be gender-dependent in the Servicemembers with PTSD. Glucocorticoid and/or estrogen receptors may play a role in the regulation of mtDNAcn. The sex-related difference of mtDNAcn may serve as a PTSD biomarker for females.

Aberrant white matter microstructure evaluation by automated fiber quantification in typhoon-related post-traumatic stress disorder.

Super typhoons can lead to post-traumatic stress disorder (PTSD), which can adversely affect a person's mental health after a disaster. Neuroimaging studies suggest that patients with PTSD may have post-exposure abnormalities of the white matter. However, little is known about these defects, if they are localized to specific regions of the white matter fibers, or whether they may be potential biomarkers for PTSD. Typhoon survivors with PTSD (n = 27), trauma-exposed controls (TEC) (n = 33), and healthy controls (HCs) (n = 30) were enrolled. We used automated fiber quantification (AFQ) to process the participants' DTI and compared diffusion metrics among the three groups. To evaluate diagnostic value, we used support vector machine (SVM) and a random forest (RF) classifier to build a machine learning model. White matter fiber segmentation between the three groups was found to be statistically significant for the fractional anisotropy (FA) value of the right anterior thalamic radiation (ATR) (26-50 nodes) and right uncinate fasciculus (UF) (60-72 nodes) (FDR correction, p < 0.05). By analyzing the characteristics of the machine learning model, the two most important variables were the right ATR and right UF for differentiating PTSD and trauma-exposed controls (TEC) from the healthy controls (HC). In addition, the left cingulum cingulate and left UF were the most critical variables in the differentiation of PTSD and TEC. AFQ with machine learning can localize abnormalities in specific regions of white matter fibers. These regions may be used as a diagnostic biomarker for PTSD.

Effects of a diet low in excitotoxins on PTSD symptoms and related biomarkers

ABSTRACT Post-traumatic stress disorder (PTSD) develops after trauma exposure and involves symptoms of avoidance, intrusive re-experiencing, mood and cognitive dysfunction, and hypervigilance. PTSD is often comorbid with Gulf War Illness (GWI), a neurological condition involving widespread pain, cognitive dysfunction, digestive problems, and other symptoms, in Gulf War veterans. PTSD tends to be more severe when comorbid with GWI. Low cortisol and elevated homocysteine levels have been found in PTSD, making them potential PTSD biomarkers. The low-glutamate diet, which aims to reduce excitotoxicity by eliminating the consumption of free glutamate and aspartate, has been shown to significantly reduce GWI and PTSD symptoms. This study examined whether changes in serum cortisol and homocysteine are associated with reduced PTSD severity in veterans with GWI after one month on the low-glutamate diet, and whether reducing the consumption of dietary excitotoxins was associated changes in PTSD and serum biomarkers. Data were analyzed for 33 veterans. No serum biomarkers significantly changed post-diet; however, cortisol increased as dietary excitotoxin consumption decreased, which held in a multivariable linear regression after adjustment for sex. Reduced dietary excitotoxin consumption was also associated with reduced hyperarousal symptoms, which held in a multivariable linear regression after adjustment for sex. Cortisol increase was associated with reduced avoidance symptoms after adjustment for change in BMI, and was marginally associated with overall PTSD reduction. Change in homocysteine was not significantly related to dietary adherence nor change in PTSD. Results suggest that reducing the consumption of dietary excitotoxins may normalize cortisol levels, which has been associated with alleviating PTSD.

Blood transcriptome analysis: Ferroptosis and potential inflammatory pathways in post-traumatic stress disorder.

BackgroundTranscriptome-wide analysis of peripheral blood in post-traumatic stress disorder (PTSD) indicates widespread changes in immune-related pathways and function. Ferroptosis, an iron-dependent regulated cell death, is closely related to oxidative stress. However, little is known as to whether ferroptosis plays a role in PTSD.MethodsWe conducted a comprehensive analysis of combined data from six independent peripheral blood transcriptional studies in the Gene Expression Omnibus (GEO) database, covering PTSD and control individuals. Differentially expressed genes (DEGs) were extracted by comparing PTSD patients with control individuals, from which 29 ferroptosis-related genes (FRGs) were cross-matched and obtained. The weighted gene co-expression network analysis (WGCNA), the Extreme Gradient Boosting (XGBoost) model with Bayesian Optimization, and the least absolute shrinkage and selection operator (LASSO) Cox regression were utilized to construct a PTSD prediction model. Single-sample Gene Set Enrichment Analysis (ssGSEA) and CIBERSORT revealed the disturbed immunologic state in PTSD high-risk patients.ResultsThree crucial FRGs (ACSL4, ACO1, and GSS) were identified and used to establish a predictive model of PTSD. The receiver operating characteristic (ROC) curve verifies its risk prediction ability. Remarkably, ssGSEA and CIBERSORT demonstrated changes in cellular immunity and antigen presentation depending on the FRGs model.ConclusionThese findings collectively provide evidence that ferroptosis may change immune status in PTSD and be related to the occurrence of PTSD, which may help delineate mechanisms and discover treatment biomarkers for PTSD.

Adiponectin gene polymorphisms and posttraumatic stress disorder symptoms among female rape survivors: an exploratory study

ABSTRACT Background: Rape is a common traumatic event which may result in the development of posttraumatic stress disorder (PTSD), yet few studies have investigated risk biomarkers in sexually traumatised individuals. Adiponectin is a novel cytokine within inflammatory and cardiometabolic pathways with evidence of involvement in PTSD. Objective: This prospective exploratory study in a sample of female rape survivors investigated the association of single nucleotide polymorphisms (SNPs) in the adiponectin gene (ADIPOQ) and posttraumatic stress symptom (PTSS) severity, and the interaction of these SNPs of interest with childhood trauma in modifying the association with PTSS severity. Method: The study involved 455 rape-exposed black South African women (mean age (SD), 25.3 years (±5.5)) recruited within 20 days of being raped. PTSS was assessed using the Davidson Trauma Scale (DTS) and childhood trauma was assessed using a modified version of the Childhood Trauma Scale-Short Form Questionnaire. Eight ADIPOQ SNPs (rs17300539, rs16861194, rs16861205, rs2241766, rs6444174, rs822395, rs1501299, rs1403697) were genotyped using KASP. Mixed linear regression models were used to test additive associations of ADIPOQ SNPs and PTSS severity at baseline, 3 and 6 months following rape. Results: The mean DTS score post-sexual assault was high (71.3 ± 31.5), with a decrease in PTSS severity shown over time for all genotypes. rs6444174TT genotype was inversely associated with baseline PTSS in the unadjusted model (β = −13.6, 95% CI [−25.1; −2.1], p = .021). However, no genotype was shown to be significantly associated with change in PTSS severity over time and therefore ADIPOQ SNP x childhood trauma interaction was not further investigated. Conclusion: None of the ADIPOQ SNPs selected for investigation in this population were shown to be associated with change in PTSS severity over a 6-month period and therefore their clinical utility as risk biomarkers for rape-related PTSD appears limited. These SNPs should be further investigated in possible gene-gene and gene-environment interactions.

Epigenome-wide association study of posttraumatic stress disorder identifies novel loci in U.S. military veterans

Posttraumatic stress disorder (PTSD) is a chronic and disabling psychiatric disorder prevalent in military veterans. Epigenetic mechanisms have been implicated in the etiology of PTSD, with DNA methylation being the most studied to identify novel molecular biomarkers associated with this disorder. We performed one of the largest single-sample epigenome-wide association studies (EWAS) of PTSD to date. Our sample included 1135 male European–American U.S. veterans who participated in the National Health and Resilience in Veterans Study (NHRVS). DNA was collected from saliva samples and the Illumina HumanMethylation EPIC BeadChip was used for the methylation analysis. PTSD was assessed using the PTSD Checklist. An EWAS was conducted using linear regression adjusted for age, cell-type proportions, first 10 principal components, and smoking status. After Bonferroni correction, we identified six genome-wide significant (GWS) CpG sites associated with past-month PTSD and three CpGs with lifetime PTSD (prange = 10−10–10−8). These CpG sites map to genes involved in immune function, transcription regulation, axonal guidance, cell signaling, and protein binding. Among these, SENP7, which is involved in transcription regulation and has been linked to risk-taking behavior and alcohol consumption in genome-wide association studies, replicated in an independent veteran cohort and was downregulated in medial orbitofrontal cortex of PTSD postmortem brain tissue. These findings suggest potential epigenetic biomarkers of PTSD that may help inform the pathophysiology of this disorder in veterans and other trauma-affected populations.

Altered gene expression and PTSD symptom dimensions in World Trade Center responders.

Despite experiencing a significant trauma, only a subset of World Trade Center (WTC) rescue and recovery workers developed posttraumatic stress disorder (PTSD). Identification of biomarkers is critical to the development of targeted interventions for treating disaster responders and potentially preventing the development of PTSD in this population. Analysis of gene expression from these individuals can help in identifying biomarkers of PTSD. We established a well-phenotyped sample of 371 WTC responders, recruited from a longitudinal WTC responder cohort using stratified random sampling, by obtaining blood, self-reported and clinical interview data. Using bulk RNA-sequencing from whole blood, we examined the association between gene expression and WTC-related PTSD symptom severity on (i) highest lifetime Clinician-Administered PTSD Scale (CAPS) score, (ii) past-month CAPS score, and (iii) PTSD symptom dimensions using a 5-factor model of re-experiencing, avoidance, emotional numbing, dysphoric arousal and anxious arousal symptoms. We corrected for sex, age, genotype-derived principal components and surrogate variables. Finally, we performed a meta-analysis with existing PTSD studies (total N = 1016), using case/control status as the predictor and correcting for these variables. We identified 66 genes significantly associated with total highest lifetime CAPS score (FDR-corrected p < 0.05), and 31 genes associated with total past-month CAPS score. Our more granular analyses of PTSD symptom dimensions identified additional genes that did not reach statistical significance in our analyses with total CAPS scores. In particular, we identified 82 genes significantly associated with lifetime anxious arousal symptoms. Several genes significantly associated with multiple PTSD symptom dimensions and total lifetime CAPS score (SERPINA1, RPS6KA1, and STAT3) have been previously associated with PTSD. Geneset enrichment of these findings has identified pathways significant in metabolism, immune signaling, other psychiatric disorders, neurological signaling, and cellular structure. Our meta-analysis revealed 10 genes that reached genome-wide significance, all of which were downregulated in cases compared to controls (CIRBP, TMSB10, FCGRT, CLIC1, RPS6KB2, HNRNPUL1, ALDOA, NACA, ZNF429 and COPE). Additionally, cellular deconvolution highlighted an enrichment in CD4 T cells and eosinophils in responders with PTSD compared to controls. The distinction in significant genes between total lifetime CAPS score and the anxious arousal symptom dimension of PTSD highlights a potential biological difference in the mechanism underlying the heterogeneity of the PTSD phenotype. Future studies should be clear about methods used to analyze PTSD status, as phenotypes based on PTSD symptom dimensions may yield different gene sets than combined CAPS score analysis. Potential biomarkers implicated from our meta-analysis may help improve therapeutic target development for PTSD.

Classification of posttraumatic stress disorder and related outcomes in women veterans using magnetoencephalography.

Women veterans represent a unique population whose experiences and neurobiology differ from that of their male counterparts. Thus, while previous research has demonstrated the utility of synchronous neural interactions (SNI) as a biomarker of posttraumatic stress disorder (PTSD) in male veterans, the utility of SNI as a biomarker of PTSD in women veterans is unclear. Here we extend that line of research to evaluate classification of women veterans with and without PTSD and other trauma-related outcomes based on functional connectivity using magnetoencephalography (MEG). A total of 121 U.S. women veterans completed diagnostic interviews and underwent a task-free MEG scan from which SNI was computed. Linear discriminant analysis was used to classify PTSD and control groups according to SNI. That discriminant function was then used to classify each individual in the partial recovery and full recovery diagnostic groups as PTSD or control. All individuals were classified correctly (100% accuracy) according to their SNI in their PTSD and control groups. Seventy-seven percent of the full recovery group and 69% of the partial recovery group were classified as control. Individual staging in PTSD recovery was captured by the Mahalanobis D2 distances from the center of the control and PTSD centroid clusters. These findings provide compelling evidence supporting the utility of task-free SNI as a biomarker of PTSD and related outcomes in women veterans.